Is there an online tool that can predict accuracy given only the dataset?
If I get correctly what you're asking, the answer is no, there is no way of knowing in advance how good a model would perform in a dataset without training a model on it. That's the whole point of data science, you try, you analyse the results, you try again using the knowledge you got from your previous attempts. It would be nice to hack the whole field and know in advance what to do to get the perfect model but it is rather unrealistic. 

Anyway, there are some standard steps that usually help understanding if you're going on the right direction. For example creating a random benchmark to see how much your model is better than a random one. To create such benchmark you definitely don't need a specific tool, all main programming languages provide built in function to generate random numbers, and that's basically all that you need. For example in python you could do something like:

import numpy as np

# create 30 true labels 
y_true = [1,2,3]*10

# generate 30 random labels
y_random = np.random.randint(0,3,size=30)

# calculate accuracy for random model
acc_random = sum(y_true==y_random)/len(y_true) 


Another thing that is worth to mention is that in the academy people are pushing more and more to use the same datasets in order to have comparable results. for example if you're trying to train an architecture that should be used for images classification, some golden dataset you must test your models on are the MNIST ones. It is well known that on these datasets a good model should achieve more than 99% accuracy, therefore this is an establish lower bound for these datasets.